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SYSTEMATIC REVIEW article

Front. Educ.
Sec. Higher Education
Volume 9 - 2024 | doi: 10.3389/feduc.2024.1492308
This article is part of the Research Topic AI's Impact on Higher Education: Transforming Research, Teaching, and Learning View all 3 articles

Exploring the Applications of Artificial Intelligence in Mechanical Engineering Education

Provisionally accepted
Mohannad Alghazo Mohannad Alghazo Vian Ahmed Vian Ahmed Zied BAHROUN Zied BAHROUN *
  • American University of Sharjah, Sharjah, United Arab Emirates

The final, formatted version of the article will be published soon.

    In an era marked by technological sophistication, Artificial Intelligence (AI) is increasingly being integrated into various fields, including Mechanical Engineering Education (MEE). This review paper presents a systematic examination of scientific publications in this field, spanning from 2018 to 2023. Utilizing the PRISMA framework, 228 research papers were selected and analyzed to identify research gaps and future directions in AI's application within the MEE discipline. The diverse applications of AI in MEE identified include personalized learning, smart tutoring systems, digitizing engineering drawings, enhancing simulation and assessment, and boosting student motivation and engagement. Additionally, a bibliometric analysis of AI in MEE was conducted, examining its role in different aspects of MEE, interdisciplinary collaboration, geographic distribution, and research focus. Accordingly, the scope of this review encompasses a comprehensive content analysis and bibliometric evaluation of AI applications in MEE. This review systematically identifies current applications of AI, maps research trends, and analyzes publication data to highlight interdisciplinary collaborations and geographical distributions. Furthermore, this study identifies critical research gaps and offers actionable recommendations, emphasizing future directions such as advancing Generative Artificial Intelligence (GAI) applications in MEE and reshaping curricula to integrate AI-based learning tools. The findings provide valuable insights to support stakeholders in evolving MEE to meet industry needs and enhance educational outcomes.

    Keywords: Mechanical Engineering, Education, artificial intelligence, machine learning, Educational Automation including "Mechanical", "Engineering", "Education", "artificial intelligence"

    Received: 06 Sep 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 Alghazo, Ahmed and BAHROUN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zied BAHROUN, American University of Sharjah, Sharjah, United Arab Emirates

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.